Common image filtering techniques include image smoothing and image sharpening. Smoothing is a technique for reducing certain types of noise in an image. Sharpening is a technique for sharpening the edges within an image to improve the visual quality of the image. Non-selective filters treat all features in an image in the same way. Thus, flat regions of an image are treated in the same way as edge regions. Non-selective filters tend to smooth edge regions and/or to amplify noise. Selective filters (e.g., selective smoothing and/or selective sharpening filters), on the other hand, overcome the disadvantages of non-selective filters by applying the filtering function only to selected types features while preserving non-selected types features.
Some selective denoising techniques selectively smooth flat regions of an image, while leaving edge regions un-smoothed. Similarly, some selective sharpening methods selectively sharpen edge regions without sharpening flat regions. Some other denoising techniques, however, combine selective image sharpening and selective image smoothing in a single filter. For example, in one approach, a pre-existing selective smoothing filter is used to derive a matching non-selective smoothing filter by disabling the selectivity mechanism of the selective smoothing filter. The difference of the pre-existing and derived filters is substituted into the high-pass filter operation of an unsharp masking filter operation to form the image processing filter.
One example of an edge preserving, selective smoothing filter is an anisotropic diffusion filter. Another example of a selective filter is a bilateral filter. One exemplary non-iterative, locally adaptive bilateral filter for removing noise from images corresponds to a weighted average of the local neighborhood pixels, where the weights are computed based on spatial distances between a center pixel and pixels neighboring the center pixel. Another type of selective filter is a robust filter that includes an influence function that is designed to reduce the influence of outliers (i.e., neighboring pixels with intensities that are very different from the intensity of the pixel being filtered).
In general, image filters are built based on models for approximating the local behavior of images. Low-resolution (e.g., less than 1 Mpixel) scans and images that are acquired by low-resolution cameras may be approximated by piece-wise constant images, which consist of patches of constant colors separated by abrupt edges. Intermediate-resolution (e.g., up to about 2 Mpixels) scans and pictures are better approximated by piece-wise linear images, which consist of patches of constant gradients. Local regions of images of higher resolution (e.g., greater than 2 Mpixels), however, tend not to be represented accurately by piece-wise linear or piece-wise constant approximations.